Probing molecular tracers in geobiological systems using imaging mass spectrometry
Dissertation
zur Erlangung des mathematisch‐naturwissenschaftlichen Doktorgrades
"Doctor rerum naturalium"
der Georg‐August‐Universität Göttingen
im Promotionsprogramm Geowissenschaften der Georg‐August University School of Science (GAUSS)
vorgelegt von Tim Leefmann
aus Lüneburg Göttingen 2013
Prof. Dr. Joachim Reitner, Abteilung Geobiologie, GZG
Mitglieder der Prüfungskommission
Dr. Martin Blumenberg, Abteilung Geobiologie, GZG PD Dr. Gernot Arp, Abteilung Geobiologie, GZG
Prof. Dr. Bent T. Hansen, Abteilung Isotopengeologie, GZG Prof. Dr. Andreas Pack, Abteilung Isotopengeologie, GZG
Referent
Prof. Dr. Volker Thiel Korreferent
Prof. Dr. Joachim Reitner
Tag der mündlichen Prüfung 11.04.2013
I cordially thank Volker Thiel for the opportunity to work in this project on this
“unconventional” mass spectrometric technique and all the scientific support throughout the last years that greatly helped in finishing this thesis. I furthermore thank Joachim Reitner for being the co‐advisor and for the help with the SEM analyses. For being members of the thesis committee I thank Martin Blumenberg, Gernot Arp, Bent T. Hansen and Andreas Pack.
I am grateful to Christine Heim for many constructive discussions during field campaigns in Äspö and Borås. Sincere thanks are given to Peter Sjövall, Jukka Lausmaa, Sandra Siljeström, Anastasiia Kryvenda, Danny Ionescu, Christine Heim, Martin Blumenberg, Joachim Reitner, and Volker Thiel for their constructive comments on the original manuscripts.
For support during laboratory work in Göttingen and Borås I thank Cornelia Conradt, Dorothea Hause‐Reitner, Wolfgang Dröse, Alexander Satmari, Peter Sjövall, Jukka Lausmaa, Sandra Siljeström, Per Borchard, and Jakob Malm. The field work in Äspö Hard Rock Laboratory would not have been possible without the logistical support from Emmeli Johannson, Magnus Kronberg, Linda Alakangas, Mats Lundqvist, and Karin Nilsson.
Thomas Bode was always helpful with any kind of computer related problem.
Thanks go also to Lothar Laake and his team for constructing the UV‐ozone cleaning apparatus and the flow reactors, to Richard Splivallo for the provision of truffle samples, and to Jan Bauermeister for proofreading.
For creating a pleasant working atmosphere in and around room 311 in the last years I would like to thank Juliane Germer, Cornelia Conradt, Katharina Liebenau, Christine Berndmeyer, Jan Bauermeister, and Andrea Hagemann. This thesis was compiled within the frame of the DFG research unit 571, subproject 6 “ToF‐SIMS imaging mass spectrometry of microbial systems”.
Heim C., Sjövall P., Lausmaa J., Leefmann T., Thiel V. (2009) Spectral characterization of eight glycerolipids and their detection in natural samples using time‐of‐flight secondary ion mass spectrometry. Rapid Communications in Mass Spectrometry, 23, 2741‐2753.
Leefmann T., Heim C., Siljeström S., Blumenberg M., Sjövall P., Thiel V. (2013) Spectral characterization of ten cyclic lipids using time‐of‐flight secondary ion mass spectrometry. Rapid Communications in Mass Spectrometry, 27, 565‐581.
Leefmann T., Heim C., Kryvenda A., Siljeström S., Sjövall P., Thiel V. (2013) Biomarker imaging of single diatom cells in a microbial mat using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Organic Geochemistry, 57, 23‐33.
Leefmann T., Heim C., Lausmaa J., Sjövall P., Ionescu D., Reitner J., Thiel V. An imaging mass spectrometry study on the formation of conditioning films and biofilms in the subsurface (Äspö Hard Rock Laboratory, SE Sweden). In preparation for Geomicrobiology Journal.
Chapter 1 ‐ Introduction ... 1
1.1. Imaging mass spectrometry in the field of geobiology ... 1
1.2. Secondary ion mass spectrometry ... 2
1.3. Äspö Hard Rock Laboratory ... 7
1.4. ToF‐SIMS as an new analytical approach for geobiological samples ... 8
1.5. Introduction to the following chapters ... 9
References ... 10
Chapter 2 ‐ “Spectral characterization of eight glycerolipids and their detection in natural samples using time‐of‐flight secondary ion mass spectrometry” ... 13
2.1. Abstract ... 13
2.2. Introduction ... 14
2.3. Experimental ... 16
2.4. Results and discussion ... 19
2.5. Conclusions ... 33
Acknowledgements ... 34
References ... 35
Chapter 3 ‐ “Spectral characterization of ten cyclic lipids using time‐of‐flight secondary ion mass spectrometry” ... 38
3.1. Abstract ... 38
3.2. Introduction ... 39
3.3. Experimental ... 41
3.4. Results and discussion ... 44
3.5. Conclusions ... 65
Acknowledgments ... 67
References ... 67
4.1. Abstract ... 72
4.2. Introduction ... 72
4.3. Materials & Methods ... 74
4.4. Results ... 76
4.5. Discussion ... 90
4.6. Conclusions ... 92
Acknowledgements ... 93
References ... 93
Chapter 5 ‐ “An imaging mass spectrometry study on the formation of conditioning films and biofilms in the subsurface (Äspö Hard Rock Laboratory, SE Sweden)”... 98
5.1. Abstract ... 98
5.2. Introduction ... 99
5.3. Materials & Methods ... 101
5.4. Results ... 104
5.5. Discussion ... 107
5.6. Conclusions ... 110
Acknowledgements ... 111
References ... 111
Chapter 6 ‐ Summary and conclusions ... 114
1.
Introduction
1.1. Imaging mass spectrometry in the field of geobiology
Geobiological systems are characterized by the close interaction of the biosphere and the geosphere. The key players in these systems are µm‐sized organisms which are involved in the formation and transformation of organic matter, gases, minerals, and rocks (Thiel and Sjövall, 2011). For the investigation of recent geobiological systems and their fossil equivalents high‐resolution analytical techniques proved to be particularly advantageous (Orphan and House, 2009), due to the low sizes of the involved organisms and their chemical imprints in the fossil record. Whereas high‐
resolution, mass spectrometric techniques such as laser ablation inductively coupled plasma mass spectrometry and ion microprobe are well established for tracing elemental and isotopic anomalies (Sylvester, 2006; Boxer et al., 2009; Orphan and House, 2009), equivalent methods for the chemical analysis of organic molecules have not reached the same level of applicability to environmental samples.
However, in the last decade, imaging mass spectrometric techniques have increasingly been used for the spatially‐highly resolved analysis of ions from large organic molecules (Kollmer, 2004) in biological (Brunelle and Laprévote, 2009;
Passarelli and Winograd, 2011; Sjövall et al., 2004; Winograd and Garrison, 2010) and, to a lesser degree, in geobiological (Heim et al., 2012; Siljeström et al., 2009;
Siljeström et al., 2010; Thiel et al., 2007a; Thiel et al., 2007b; Toporski et al., 2002) , materials. For geobiological applications, the detection of taxonomically specific organic molecules is particularly desirable, as such so‐called "biomarkers" can be preserved in sedimentary rocks and thus allow for tracing back organisms and reconstructing ecosystems in the fossil record (Brocks and Peason, 2005). The term biomarker applies to organic molecules from different compound classes, including lipids, nucleic acids, amino acids, lignins, and carbohydrates. While nucleic and amino acids usually have a higher taxonomic specificity as compared to other compound classes, lipids are far more resistant to degradation, and are thus the most widely used biomarkers in the analysis of fossil geobiological systems (Summons et al., 2008).
Biomarkers are usually analyzed by extraction‐based techniques such as coupled gas chromatography‐ or liquid chromatography–mass spectrometry (GC‐
MS/LC‐MS). These methods can provide highly detailed information on the structure of the biomarkers, but the appendant extraction procedure destroys the physical integrity of the sample and thus limits the applicability of these techniques to complex, heterogeneous samples. Furthermore, the destructing sample preparation complicates the combined application of different analytical techniques and thus substantially reduces the information obtainable from a given sample. Imaging mass spectrometric techniques as static time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS; Benninghoven, 1994) can in theory provide non‐destructive and laterally highly resolving analyses for organic molecules. In this thesis project, ToF‐
SIMS is therefore tested for its applicability to complex environmental samples.
1.2. Secondary ion mass spectrometry
In secondary ion mass spectrometry (SIMS), a high‐energy primary ion beam is used to emit particles from the surface of solid state materials. The kinetic energy of the impacting primary ion is distributed in the sample through a collision cascade, and is leading to the breaking of bonds and thus to the emission of particles from the sample. Most of these particles are emitted as neutrals (Vickerman, 2001) and only a small fraction (10‐6‐10‐1; Belu et al., 2003) as charged particles, i.e. secondary ions, which can be analyzed by means of mass spectrometry. By rastering the primary ion beam over the sample surface, SIMS allows for imaging the lateral distribution of specific secondary ions on the surface of the analyzed material with lateral resolution up to the submicron scale (Hagenhoff, 2000). SIMS can be used for the study of elemental ions and their isotopes as well as for the analysis of ions of complex molecules, but each application requires a specific instrument design (Boxer et al., 2009). Molecular ion species are best analyzed by static SIMS (Vickerman, 2001) using the ToF‐SIMS technique (Benninghoven, 1994).
1.2.1. Theoratical background of static SIMS
The secondary ion yield of a chemical species obtained in a SIMS experiment is defined through the basic SIMS equation
(Vickerman, 2001)
where is the primary particle flux, is the total yield of sputtered particles of species , is the ionization probability of , is the concentration of in the uppermost monolayer, and is the transmission of the spectrometer. The yield of sputtered particles increases with flux, mass, charge, and energy of the primary ions.
However, as sputtering is a damaging process, the yield of characteristic particles will decrease with increasing analysis time. For a chemical species this decrease with analysis time is given through
where is the secondary ion intensity, is the secondary ion intensity at the beginning of the experiment, is the primary particle flux, and is the damage cross section (which is specific for the chemical species analyzed). In a static SIMS analysis, the primary ion dose density (PIDD) applied to the surface is kept below 1012‐1013 ions cm‐2, so that less than 1% of the uppermost monolayer of atoms or molecules is impacted by the ion bombardment (Vickerman, 2001) and no significant fraction of the surface is damaged (Thiel and Sjövall, 2011). Keeping the PIDD below the static limit is crucial for the analysis of molecular ions, as those are susceptible to decomposition under intense ion bombardment.
The ionization of the particles occurs at or close to the emission from the sample surface (Vickerman, 2001). The ionization probability is thus highly dependent on the electronic state of the chemical environment, i.e. the matrix, from which the particles are emitted. This so‐called matrix effect can lead to considerable variations in the yield of secondary ions and thus hampers quantitative analyses by SIMS (Fletcher and Vickerman, 2013; Vickerman, 2011).
Emitted secondary ions of mass m are accelerated by a potential U and travel through a flight tube to the detector of the ToF‐SIMS instrument. The equation for the kinetic energy is
2 2
with z being the charge and v the velocity of the ion, L being the length of the flight path, and t being the travel time of the ion to the detector. By rearranging this equation to
2
it becomes apparent that the m/z ratio of a given ion can be calculated from its travel time t at constant U and L.
1.2.2. ToF‐SIMS technique
A schematic sketch of a ToF‐SIMS instrument is shown in Figure 1‐1. The main components that determine the field of application of the SIMS instrument are the primary ion sources and the analyzer. Primary ion sources used in ToF‐SIMS instruments include surface ionization sources (e.g. Cs+), gas (cluster) ion sources (e.g. Ar+, O2+, C60+), and liquid metal ion guns (LMIG; e.g. Ga+, Au+, Bi+, Bin+, Aun+).
Figure 1‐1. The ToF‐SIMS instrument used in this study. (a) Schematic sketch and (b) photo of the ION‐
TOF IV instrument at the SP Technical Research Institute of Sweden.
The advantage of LMIGs is that they are capable of producing primary ion beams with diameters as small as 50 nm (Winograd and Garrison, 2010) while maintaining a high primary ion current. They can thus provide high image resolution combined with a high secondary ion yield. The application of cluster ions such as Bin+ or Aun+
significantly increases the yield of high‐mass secondary ions as compared to atomic primary ions (e.g. Au+ or Bi+; Touboul et al., 2005), as on impact their energy is deposited closer to the sample surface (Colla et al., 2000; Thiel and Sjövall, 2011), which leads to less fragmentation of the emitted molecules.
The continuous beam produced by the ion source is electronically chopped into pulses. In cluster ion sources, a secondary chopper or a Wien‐filter is used to remove
PRIMARY ION SOURCE (Bi3+) PRIMARY ION SOURCE (C60+)
DETECTOR
REFLECTRON
FLIGHT TUBE
LOAD LOCK ELECTRON FLOOD GUN
EXTRACTOR
SAMPLE GATE
(a) (b)
ions of undesired cluster size. If a high mass resolution is required, a buncher is used to compress the pulses, so that all ions of a pulse arrive simultaneously at the sample surface (bunched mode; Sodhi, 2004). If analyses with high lateral resolution are desired, the buncher is turned off and lower ion currents and longer pulses are used (burst alignment mode; Thiel and Sjövall, 2011).
The secondary ions enter the flight tube through the extractor. A reflectron, or ion mirror, positioned in the middle of the flight path (Figure 1‐1) corrects secondary ions for small variations in their kinetic energy (Belu et al., 2003). As a result, all ions of the same species arrive at the detector simultaneously.
1.2.3. Sample preparation for ToF‐SIMS
All kinds of samples with solid surfaces can be analyzed by means of ToF‐SIMS.
However, two major requirements have to be met in order to generate interpretable data of the surface chemistry:
(i) The sample surface has to be flat to avoid topographic effects such as peak splitting or broadening in the mass spectrum (Thiel and Sjövall, 2011).
(ii) The sample has to be contaminant‐free, as all contaminants adhering to the surface can have a major influence on the resulting spectrum.
For the analysis of biological samples such as microbial mats, both requirements can be fulfilled by preparing cryosections. The samples are embedded, frozen in isopentane, sliced into thin sections using a cryomicrotom, and placed on microscopic slides (Figure 1‐2). If the ToF‐SIMS is combined with other microscopic techniques, microscopic slides with premarked grids allow for easy relocalization of the analyzed area. The embedding agent has to be carefully chosen to avoid isobaric interferences of ions from the agent and from the target compounds. Other protocols commonly used for the preparation of biological materials include freeze fracturing (Lanekoff et al., 2011; Lanekoff et al., 2010) and chemical imprinting on Ag surfaces (Sjövall et al., 2003). A third method for producing flat, contaminant‐free surfaces is the use of a sputter ion gun, e.g. C60+, capable of performing molecular depth profiling of biological materials (Fletcher et al., 2007; Fletcher and Vickerman, 2013).
Figure 1‐2. Schematic illustration of the sample preparation protocol used for microbial mat samples in this thesis.
For mineralized geobiological samples, such as microbialites, sample sections with flat surfaces can be produced by the use of precision saws (Heim et al. 2012;
Heindel et al., 2012). Due to the water used for cooling of the sawblade during sectioning, the sample surfaces are susceptible to contamination. Rougher but less contaminated surfaces, as compared to the sawed samples, may be generated by the use of microdrill cores, which are broken from the mineralized sample immediately before analysis in the ToF‐SIMS instrument (Thiel et al., 2007a).
For keeping the surface to be analysed as free as possible of contaminations, solvent rinsing has to be applied to all tools coming in contact with the samples, e.g.
sawblades or microtome knives, independently of the protocol used for sample preparation.
1.3. Äspö Hard Rock Laboratory
The geobiological samples analyzed in this thesis were collected in the Äspö Hard Rock Laboratory (Äspö HRL), located at the coast of the Baltic Sea 20 km north of Oskarshamn, SE‐Sweden. Beside several above‐ground facilities, the Äspö HRL comprises a 3.6 km long research tunnel that extends down to a maximum depth of 450 m below sea level (bsl). The tunnel was excavated by the Swedish Nuclear Fuel and Waste Management Company (SKB) and serves as a study site for the long‐term deposition of radioactive waste. It further provides convenient access to different subterranean ecosystems. Consequently, microbial life in the tunnel has been in the focus of intense geobiological research (Anderson et al., 2006; Anderson and Pedersen, 2003; Kotelnikova and Pedersen, 1998; Pedersen, 1997), e.g. in the context of potential corrosion of canisters enclosing the nuclear waste through acids produced by microorganisms (Pedersen, 1999).
Figure 1‐3. Flow reactors installed in Äspö HRL. (a) Sketch of Äspö HRL showing locations of flow reactors connected to fluid outflows (b) at 507 m (69 m bsl; dark and air tight) and (c) at 1327 m (183 m bsl, artificially illuminated and aerated) distance from the tunnel entrance.
Within the frame of the DFG research unit 571 “Geobiology of organo‐ and biofilms”, the microbial ecosystems of the Äspo tunnel have been further investigated through the installation of flow reactors at four different tapped fluid outflows from rock fractures in the tunnel walls (Heim, 2010). These flow reactors provide different environmental conditions (oxic/anoxic, light/dark) and serve for growing and collecting microorganisms of the pristine, subterranean biosphere. The samples analyzed in the frame of this thesis were obtained from two flow reactors installed at 507 m (69m bsl) and 1327 m (183m bsl) distance from the tunnel entrance (Figure 1‐3), respectively.
1.4. ToF‐SIMS as an new analytical approach for geobiological samples
The imaging capability of ToF‐SIMS may offer a new analytical approach for the direct study of organic ions in geobiological samples.
Conventional biomarker analyses by liquid extraction‐based techniques (GC‐
MS, LC‐MS) are well suited to characterize the biomarker content of bulk environmental samples, but the appendant extraction procedure destroys the physical integrity of the sample and the information on the localization of the biomarkers within the sample gets lost. When analyzing heterogeneous geobiological samples, e.g. mineralizing mats comprised of complex microbial consortia, the exact source of the biomarkers may thus remain unclear. In contrast to extraction‐based techniques, ToF‐SIMS can in theory provide molecular analysis at the microscopic level and may thus reveal the different microbial sources of biomarkers in environmental samples. Consequently, the limits of the latteral resolution of ToF‐SIMS imaging have to be explored on environmental samples. Especially the applicability of ToF‐SIMS for analyzing the biomarker content of individual microbial cells has to be tested.
However, the application of ToF‐SIMS to complex environmental samples is currently hampered by certain drawbacks.
Due to the ion formation processes, mass spectra obtained by SIMS are not necessarily simmilar to mass spectra obtained by other ionization techniques (Spool, 2004). The reliable detection of a particular compound in SIMS therefore requires a corresponding SIMS reference spectrum of the pure
standard compound. However, only a limited number of such SIMS reference spectra for biogeochemically relevant compounds have been published as yet.
In most ToF‐SIMS systems recently used, the ion formation is not decoupled from the actual analysis. As a result all ion species produced in a ToF‐SIMS experiment are summed in one complex SIMS spectrum, which confronts the analyst with a huge amount of spectral information, especially if it is obtained from a complex environmental samples. The interpretation of such spectra is further complicated by the fact that the sensitivity of the ToF‐SIMS technique varies depending on the compound analyzed and the chemical matrix from which the ions are emitted. The analyst must therefore have an idea of which compounds are detectable in complex mixtures of organic compounds to avoid misinterpretations of SIMS spectra.
The analytical capabilities of ToF‐SIMS and the appendant drawbacks in the application to environmental samples are addressed in this thesis.
1.5. Introduction to the following chapters
For extending the ToF‐SIMS spectral library, reference spectra of relevant biogeochemical standard compounds are presented in chapter 2 and chapter 3, and their direct detection in different environmental samples is demonstrated (“Spectral characterization of eight glycerolipids and their detection in natural samples using time‐of‐flight secondary ion mass spectrometry” and “Spectral characterization of ten cyclic lipids using time‐of‐flight secondary ion mass spectrometry”).
With the knowledge gained in the standard measurements, a phototrophic microbial mat was analyzed in detail for its pigment and lipid content in chapter 4 (“Biomarker imaging of single diatom cells in a microbial mat using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS)”). Taking advantage of the non‐
destructive nature of static ToF‐SIMS, the section was imaged for fatty acids in burst alignment mode and subsequently analyzed by light microscopy to reveal sources of the fatty acids in the microbial mat. Thereby the limits of lateral resolution of the ToF‐SIMS system were tested on an environmental sample, and the biomarker content of individual diatom cells was analyzed.
The advantageous feature of static ToF‐SIMS as a surface‐sensitive technique capable of detecting smallest amounts of organic matter is used in chapter 5 (“An imaging mass spectrometry study on the formation of conditioning films and biofilms in the subsurface (Äspö Hard Rock Laboratory, SE Sweden)”) to study the formation and chemistry of thin conditioning films and biofilms forming on solid surfaces exposed to aquifer waters in Äspö HRL.
References
Anderson C. R., James R. E., Fru E. C., Kennedy C. B., Pedersen K. (2006) In situ ecological development of a bacteriogenic iron oxide‐producing microbial community from a subsurface granitic rock environment. Geobiology 4, 29–42.
Anderson C. R., Pedersen K. (2003) In situ growth of Gallionella biofilms and partitioning of lanthanides and actinides between biological material and ferric oxyhydroxides. Geobiology 1, 169–178.
Belu A. M., Graham D. J., Castner D. G. (2003) Time‐of‐flight secondary ion mass spectrometry. Biomaterials 24, 3635–3653.
Benninghoven A. (1994) Chemical analysis of inorganic and organic surfaces and thin films by static time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS).
Angewandte Chemie International Edition in English 33, 1023–1043.
Boxer S. G., Kraft M. L., Weber P. K. (2009) Advances in imaging secondary ion mass spectrometry for biological samples. Annual Review of Biophysics 38, 53–74.
Brocks J. J., Peason A. (2005) Building the biomarker tree of life. Reviews in Mineralogy and Geochemistry 59, 233–258.
Brunelle A., Laprévote O. (2009) Lipid imaging with cluster time‐of‐flight secondary ion mass spectrometry. Analytical and Bioanalytical Chemistry 393, 31–35.
Colla T., Aderjan R., Kissel R., Urbassek H. (2000) Sputtering of Au (111) induced by 16‐keV Au cluster bombardment: Spikes, craters, late emission, and fluctuations.
Physical Review B 62, 8487–8493.
Fletcher J. S., Lockyer N. P., Vaidyanathan S., Vickerman J. C. (2007) TOF‐SIMS 3D biomolecular imaging of Xenopus laevis oocytes using buckminsterfullerene (C‐60) primary ions. Analytical Chemistry 79, 2199–2206.
Fletcher J. S., Vickerman J. C. (2013) Secondary ion mass spectrometry:
characterizing complex samples in two and three dimensions. Analytical Chemistry.
85, 610–639.
Hagenhoff B. (2000) High resolution surface analysis by TOF‐SIMS. Mikrochimica Acta 132, 259–271.
Heim C. (2010) An integrated approach to the study of biosignatures in mineralizing biofilms and microbial mats. doctoral thesis, University of Göttingen, http://resolver.sub.uni‐goettingen.de/purl/?webdoc‐2799.
Heim C., Lausmaa J., Sjövall P., Toporski J., Dieing T., Simon K., Hansen B. T., Kronz A., Arp G., Reitner J., Thiel V. (2012) Ancient microbial activity recorded in fracture fillings from granitic rocks (Äspö Hard Rock Laboratory, Sweden). Geobiology 10, 280–297.
Heindel, K., Birgel, D., Brunner, B., Thiel, V., Westphal, H., Gischler, E., Ziegenbalg, S.B., Cabioch, G., Sjövall, P., Peckmann, J. (2012) Post‐glacial microbialite formation in coral reefs of the Pacific, Atlantic, and Indian Oceans. Chemical Geology 304–305, 117‐130.
Kollmer F. (2004) Cluster primary ion bombardment of organic materials. Applied Surface Science 231–232, 153–158.
Kotelnikova S., Pedersen K. (1998) Distribution and activity of methanogens and homoacetogens in deep granitic aquifers at Äspö Hard Rock Laboratory, Sweden.
FEMS Microbiology Ecology 26, 121–134.
Lanekoff I., Kurczy M. E., Adams K. L., Malm J., Karlsson R., Sjövall P., Ewing A. G.
(2011) An in situ fracture device to image lipids in single cells using ToF‐SIMS.
Surface and Interface Analysis 43, 257–260.
Lanekoff I., Kurczy M. E., Hill R., Fletcher J. S., Vickerman J. C., Winograd N., Sjövall P., Ewing A. G. (2010) Time of flight mass spectrometry imaging of samples fractured in situ with a spring‐loaded trap system. Analytical Chemistry 82, 6652–6659.
Orphan V. J., House C. H. (2009) Geobiological investigations using secondary ion mass spectrometry. Geobiology 7, 360–372.
Passarelli M. K., Winograd N. (2011) Lipid imaging with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Biochimica et Biophysica Acta (BBA) ‐ Molecular and Cell Biology of Lipids 1811, 976–990.
Pedersen K. (1997) Microbial life in deep granitic rock. FEMS Microbiology Reviews 20, 399–414.
Pedersen K. (1999) Subterranean microorganisms and radioactive waste disposal in Sweden. Engineering Geology 52, 163–176.
Siljeström S., Hode T., Lausmaa J., Sjövall P., Toporski J., Thiel V. (2009) Detection of organic biomarkers in crude oils using ToF‐SIMS. Organic Geochemistry 40, 135–
143.
Siljeström S., Lausmaa J., Sjövall P., Broman C., Thiel V., Hode T. (2010) Analysis of hopanes and steranes in single oil‐bearing fluid inclusions using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Geobiology 8, 37–44.
Sjövall P., Lausmaa J., Johansson B. (2004) Mass spectrometric imaging of lipids in brain tissue. Analytical Chemistry 76, 4271–4278.
Sjövall P., Lausmaa J., Nygren H., Carlsson L., Malmberg P. (2003) Imaging of membrane lipids in single cells by imprint‐imaging time‐of‐flight secondary ion mass spectrometry. Analytical Chemistry 75, 3429–3434.
Sodhi R. N. S. (2004) Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS).
Analyst 129, 483–487.
Spool A. M. (2004) Interpretation of static secondary ion spectra. Surface and Interface Analysis 36, 264–274.
Summons R., Albrecht P., McDonald G., Moldowan J. (2008) Molecular Biosignatures.
Space Science Reviews 135, 133–159.
Sylvester P. J. (2006) Trends in analytical developments and earth science applications in LA‐ICP‐MS and LA‐MC‐ICP‐MS for 2004 and 2005. Geostandards and Geoanalytical Research 30, 197–207.
Thiel V., Toporski J., Schumann G., Sjövall P., Lausmaa J. (2007a) Analysis of archaeal core ether lipids using Time of Flight–Secondary Ion Mass Spectrometry (ToF‐SIMS).
Geobiology 5, 75–83.
Thiel V., Heim C., Arp G., Hahmann U., Sjövall P., Lausmaa J. (2007b) Biomarkers at the microscopic range. Geobiology 5, 413–421.
Thiel V., Sjövall P. (2011) Using time‐of‐flight secondary ion mass spectrometry to study biomarkers. Annual Review of Earth and Planetary Sciences 39, 125–156.
Toporski J. K. W., Steele A., Westall F., Avci R., Martill D. M., McKay D. S. (2002) Morphologic and spectral investigation of exceptionally well‐preserved bacterial biofilms from the Oligocene Enspel formation, Germany. Geochimica et Cosmochimica Acta 66, 1773–1791.
Touboul D., Kollmer F., Niehuis E., Brunelle A., Laprévote O. (2005) Improvement of biological time‐of‐flight‐secondary ion mass spectrometry imaging with a bismuth cluster ion source. Journal of the American Society for Mass Spectrometry 16, 1608–
1618.
Vickerman J. (2011) Molecular imaging, depth profiling by mass spectrometry—
SIMS, MALDI or DESI? Analyst 136, 2199–2217.
Vickerman J. C. (2001) ToF‐SIMS. In ToF‐SIMS (eds. J. C. Vickerman, D. Briggs). IM.
Chichester, pp. 1–40.
Winograd N., Garrison B. J. (2010) Biological Cluster Mass Spectrometry. Annual Review of Physical Chemistry 61, 305–322.
2.
Spectral characterization of eight glycerolipids and their detection in natural samples using time‐of‐flight secondary ion mass spectrometry
Christine Heim, Peter Sjövall, Jukka Lausmaa, Tim Leefmann, and Volker Thiel Rapid Communications in Mass Spectrometry (2009), 23, 2741‐2753 DOI: 10.1002/rcm.4183; Reprinted with permission of John Wiley and Sons
2.1. Abstract
In recent years, time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) with cluster ion sources has opened new perspectives for the analysis of lipid biomarkers in geobiology and organic geochemistry. However, published ToF‐SIMS reference spectra of relevant compounds are still sparse, and the influence of the chemical environment (matrix) on the ionisation of molecules and their fragmentation is still not well explored. This study presents ToF‐SIMS spectra of eight glycerolipids as common target compounds in biomarker studies, namely ester‐ and ether‐bound phosphatidylethanolamine, ester‐ and ether‐bound phosphatidylcholine, ester‐bound phosphatidylglycerol, ester‐ and ether‐bound diglycerides and archaeol, obtained with a Bi3+ cluster ion source. For all of these compounds, the spectra obtained in positive and negative analytical modes showed characteristic fragments that could clearly be assigned to e.g. molecular ions, functional groups and alkyl chains. By comparison with the reference spectra, it was possible to track some of these lipids in a pre‐characterised organic extract and in cryosections of microbial mats. The results highlight the potential of ToF‐SIMS for the laterally resolved analysis of organic biomarkers in environmental materials.
The identification of the target compounds, however, may be hampered by matrix effects (e.g. adduct formation) and often require careful consideration of all spectral features and taking advantage of the molecular imaging capability of ToF‐SIMS.
2.2. Introduction
Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a surface analysis technique that allows simultaneous analysis of inorganic and organic molecules on solid surfaces (Benninghoven, 1994; Pacholski and Winograd, 1999). During the last 10–20 years, ToF‐SIMS has been used mainly in material sciences (Belu et al., 2003;
Vickerman and Briggs, 2001). The introduction of polyatomic cluster ion sources (e.g. Aun+, Bin+, C60+) has expanded the capabilities of this technique, opening new possibilities for the analysis of biological materials (Kollmer, 2004; Ostrowski et al., 2004; Sjövall et al., 2004; Touboul et al., 2004; Touboul et al., 2005) and, consequently, the application of ToF‐SIMS in geobiology and organic geochemistry (Siljeström et al., 2009; Sjövall et al., 2008; Thiel et al., 2007b). A most advantageous property of ToF‐SIMS is its ability to record the intensities of any detected ion in a given area of interest at a microscopic scale (Hagenhoff, 2000). To date, this is not possible with any of the extract‐based techniques routinely used in biomarker studies, namely GC/MS and LC/MS (coupled gas chromatography/mass spectrometry, coupled liquid chromatography/mass spectrometry). Whereas GC/MS and LC/MS are effective tools for the identification and quantification of organic compounds, it remains difficult to link the chemical information obtained to specific structures of interest in heterogeneous and structurally complex biological or geological materials. In ToF‐SIMS, identification of organic compounds is achieved mainly through precise mass determination, sometimes corroborated by the analysis of the lateral distribution of the species of interest in selected areas on the sample surface. However, the absence or as yet sparse number of published ToF‐
SIMS spectral fragmentation patterns may hamper an accurate structural assignment. Likewise, the influence of the chemical environment (matrix) on the ionisation of molecules and their fragmentation appears to be an important factor (Sostarecz et al., 2004), but is still not well explored.
Studies performed previously on reference compounds of widespread hydrocarbon biomarkers (Steele et al., 2001; Toporski et al., 2002; Toporski and Steele, 2004) showed that ToF‐SIMS spectra may, or may not, differ considerably from those obtained with conventional mass spectrometric techniques (see also Vickerman and Briggs, 2001). By comparison with pure reference compounds, it was recently proven possible to detect hydrocarbon biomarkers, namely hopanes and
steranes, in crude oils by ToF‐SIMS (Siljeström et al., 2009). Recent studies on animal tissues, eukaryotic cells or microbial consortia also revealed the potential of ToF‐SIMS with cluster ion sources for investigating the distribution of intact lipids in natural samples (Börner et al., 2007; Ostrowski et al., 2005; Ostrowski et al., 2004;
Sjövall et al., 2004; Thiel et al., 2007b). Whereas these studies focused on materials with a more or less pre‐characterised lipid content, the authors pointed out that the establishment of ToF‐SIMS reference data is a major prerequisite for the investigation of organic molecules in barely studied or unknown environmental materials.
This study presents previously unpublished ToF‐SIMS spectra of eight functionalised glycerolipids as important members of cell membrane constituents of eukaryotes, bacteria, and archaea. These lipids, or their derivatives, are commonly used as biomarkers in geo‐ and microbiology, organic geochemistry, and microbial ecology (Börner et al., 2007; Ostrowski et al., 2005; Ostrowski et al., 2004; Sjövall et al., 2004; Thiel et al., 2007b). Our work aims to provide basic information about the ToF‐SIMS fragmentation patterns of these compounds in both, positive and negative ion modes. In addition to ‘conventional’ esterbound glycerolipids, we also included a number of etherbound counterparts, as such compounds may reveal important information on the protagonists in some microbially driven ecosystems (Pancost et al., 2001). Emphasis was placed not only on high (molecular) mass species, but also on characteristic fragments that may enable a robust identification of the respective molecule, or the compound class, in a natural sample. However, detectability of a pure reference substance does not necessarily imply that the compound can be easily identified by the same features when analysed in a complex chemical matrix.
To assess the possibility of detecting such lipids in natural samples and the influence of matrix effects, ToF‐SIMS spectra of selected glycerolipids (archaeol, phosphoglycerol, diglyceride) were therefore compared with those recorded from the same, or related, compounds in an organic extract and in cryosections of microbial mats.
2.3. Experimental
Eight commercially available glycerolipids (Avanti Lipids, Sigma, Chiron) were selected for analysis, namely; (i) L‐α‐phosphatidylethanolamine (GPEtn), (ii) 1,2 di‐
O‐hexadecyl‐sn‐glycero‐3‐phosphatidylethanolamine (Diether‐GPEtn), (iii) L‐α‐
phosphatidylcholine (GPCho), (iv) 1,2‐di‐O‐dialkyl‐sn‐glycerol‐3‐
phosphatidylcholine (Diether‐GPCho), (v) L‐α‐phosphatidylglycerol (GPGro), (vi) 1‐
palmitoyl‐2‐oleoyl‐sn‐glycerol (DG), (vii) 1,2‐di‐O‐hexadecyl‐rac‐glycerol (Diether‐
DG), and (vii) 1,2‐di‐O‐phytanyl‐sn‐glycerol (archaeol), see Table 2‐1.
All reference lipids were stored cold and dark in glass vials with Teflon septa before, and between, analyses. Each reference lipid (1 mg) was dissolved in 1 mL pre‐distilled solvents (dichloromethane, n‐hexane). For ToF‐SIMS analysis, the compounds were deposited on silicon wafers. The wafers were rinsed with deionised water and cleaned in a UV ozone apparatus prior to use in order to remove any organic contaminants from the surface. Using a glass pipette, a few mL of each lipid solution (except for GPCho, see below) were placed on a silicon wafer and the organic solvent was allowed to evaporate. The deposition processes were carried out in a laminar air flow cabinet in order to avoid airborne particulate contamination. The GPCho sample was prepared as a supported lipid bilayer, according to Prinz et al., 2007. As controls, blank silicon wafers were exposed to the same conditions during sample preparation, and analysed in parallel. To assess the presence of contaminants, ToF‐SIMS spectra were also obtained from the evaporation residues of the pure solvents. These spectra were used as internal controls for a clean sample processing and are not shown here. Typically, sets of six samples were mounted on a sample holder and introduced into the vacuum chamber of the ToF‐SIMS instrument immediately after preparation.
A sample of an iron‐oxidising microbial mat dominated by Gallionella ferruginea (Pedersen, 1997) was obtained from a subterranean fluid discharge at 150 m depth in the Äspö Tunnel, SE Sweden. The mats were stored at ‐20°C prior to analysis.
A sample of a methanotrophic microbial mat was retrieved from the GHOSTDABS methane seep field on the NW’ Black Sea shelf from a water depth of 230 m (joint project BEBOP, see Acknowledgements). These microbial mats have been studied in detail for their lipid biomarker patterns (Blumenberg et al., 2004;
Michaelis et al., 2002; Pape et al., 2005; Thiel et al., 2007b).
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Table 2‐1. Glycerolipids studied and ions observed in the molecular weight range.
Compound Abbreviation Formula Exact
mass Observed
[M+H]+ Observed
[M+Na]+ Observed
[M‐H]‐ Others
L‐α‐Phosphatidylethanolamine GPEtn (16:0/18:2) C39H74NO8P 715.52 716.53 738.53 714.52 697.51 [M‐NH3]‐ L‐α‐Phosphatidylethanolamine GPEtn (18:2/18:2) C41H74NO8P 739.52 740.55 762.54 738.52 ― ― 1,2‐Di‐O‐Hexadecyl‐rac‐
phosphatidylethanolamine Diether‐GPEtn C37H78NO6P 663.56 664.65 ― 662.49 624.67 [M+Na‐C2H8NO]‐ L‐α‐Phosphatidylcholine GPCho C42H82NO8P 759.58 760.61 782.61 ― 744.54
699.47 [M‐CH3]‐ [M‐C3H9N]‐ 1,2‐O‐Dialkyl‐sn‐Glycero‐3‐
Phosphoatidylcholine Diether‐GPCho C40H85NO6P 705.60 706.75 ― 704.57 690.54
645.47 [M‐CH3]‐ [M‐C3H9N]‐ L‐α‐Phosphatidylglycerol (sodium salt) GPGro C38H74O10PNa 744.49 ― 767.51 721.58 721.58 [M‐Na]‐ 1‐Palmitoyl‐2‐Oleoyl‐sn‐glycerol DG C37H70O5 594.52 595.58 617.53 593.49 577.52 [M‐H2O]+ 1,2 Di‐O‐Hexadecyl‐rac‐glycerol Diether‐DG C35H72O3 540.55 541.63 563.58 539.54 522.59 [M‐H2O]+ 1,2‐Di‐O‐Phytanyl‐sn‐glycerol Archaeol C43H88O3 652.67 653.72 675.68 651.66 634.69 [M‐H2O]+
An organic extract of the Black Sea microbial mat was prepared as it would be typically done for LC/MS and GC/MS analyses. An aliquot of each mat (10 mg) was extracted with 15 mL of dichloromethane/methanol (3:1, 1:1, 1:3) in a Teflon‐
capped glass vial (ultrasonication, 20 min). After evaporation of the solvent mixture and re‐dissolution in pure dichloromethane, the extracts were deposited on silicon wafers and were transferred to the ToF‐SIMS instrument as described above.
For the preparation of the cryosections, the Black Sea and the Gallionella microbial mats were allowed to thaw at room temperature, and a small amount (approx. 10 mm3) of each mat sample was mounted on a cork sample holder using an embedding agent (Cryo‐Gel®, Electron Microscopy Sciences, PA, USA). The samples were frozen for 30 s in cold methyl butane at ‐150°C and immediately transferred into the cryochamber of a Leica CM 3050 S cryomicrotome (Leica Microsystems, Wetzlar, Germany) that had been pre‐cooled to ‐20°C. Using a standard steel knife (Leica Profile D), serial sections of ca. 8 mm thickness were cut, deposited on standard microscope slides (76⨯26 mm), and stored at ‐20°C in closed glass containers until analysis. Prior to transfer into the ToF‐SIMS instrument, the slides were allowed to approach room temperature with the glass container kept closed, in order to avoid condensation of water vapor on the sample.
All glassware (microscope slides, pipettes, vials, beakers, glass containers) was heated to 400°C for 2 h prior to use, and/or cleaned by thoroughly rinsing with deionised water and acetone. Solvent rinsing was also used to clean all steelware (spatula, tweezers, microtome knives) prior to use.
ToF‐SIMS images and spectra of positive and negative ions were recorded using a ToF‐SIMS IV instrument (ION‐TOF GmbH, Münster, Germany) equipped with a liquid bismuth cluster ion source. Data were acquired in bunched mode with a mass resolution of ca. M/ΔM 5000, using 25 keV Bi3+ primary ions at a pulsed current of 0.1 pA. Low‐energy electron flooding was used for charge compensation, when necessary. The analysed areas were 100⨯100 mm2 or 200⨯200mm² for references and extracts, and 500⨯500 mm² for the microbial mat cryosections. The areas were scanned in a raster pattern at 128⨯128 pixels for reference lipids and extracts, and 256⨯256 pixels for the cryosections. The acquisition times were typically between 50 s and 100 s for the pure lipid reference samples and the extracts, and 300 s to 500 s for the cryosections. All analyses were thus done under so‐called static SIMS
condition, i.e. with primary ion doses well below those where significant surface damage due to the ion bombardment starts to appear (Sjövall et al., 2008).
2.4. Results and discussion
Through comparison of the spectra from the different lipids, it was possible to determine characteristic peaks and fragmentation schemes for the respective lipid classes. In the low‐mass range, fragments specifying phospholipids in general were m/z 78.95 [PO3]‐ and 96.97 [H2PO4]‐, as previously reported (e.g. Ostrowski et al., 2005; Sjövall et al., 2004). In addition, other distinctive headgroup fragments are present in the spectra of particular phospholipid classes. Such fragments where reproducibly detected in both ester‐ and ether‐bound phospholipids and are listed in Table 2‐2. Fragmentation of ester‐bound lipids leads to prominent peaks of the corresponding fatty acid chains whereas the ether‐bound lipids exhibited weak fragment ion peaks of the alcohol side chains (Table 2‐2). In general, the fragmentation tendency of ether lipids is considerably lower compared to ester‐
bound lipids, probably due to the higher chemical stability of the ether link.
2.4.1. Phosphatidylethanolamine (GPEtn)
C39H74NO8P; exact mass 715.52 Da; source: eukaryotes (Ostrowski et al., 2005;
Ostrowski et al., 2004), bacteria (Mazzella et al., 2005)
Molecular ions are detected at m/z 716.53 and 740.55 in the positive spectrum.
Whereas the former is in accordance with the [M+H]+ ion of the actual GPEtn molecule containing one C16:0 and one C18:2 moiety (C16:0/C18:2), the latter seems to originate from GPEtn that carries two C18:2 chains (C18:2/ C18:2). The observed distribution is in good agreement with the product specification from the distributor (C16:0=24%, C18:2=60%). Both molecular ions produce sodium adducts [M+Na]+ at m/z 738.53 and 762.54, respectively. Characteristic fragments in positive mode are observed at m/z 575.49 [M–headgroup]+, 306.29 and 282.28 (Table 2‐1) and are interpreted to result from cleavage within the glycerol backbone (see fragmentation scheme, Figure 2‐1 and Table 2‐2). Specific headgroup fragments are observed at m/z 142.03 and 182.06 corresponding to [C2H9NO4P]+ and [C5H13NO4P]+.
Table 2‐2. Characteristic fragments and headgroup ions.
Abbreviation Formula Characteristic fragment ions (+) observed*
Tentative formula
Characteristic fragment ions (‐) observed*
Tentative formula
Headgroup ions (+)
observed* Formula Headgroup ions (‐)
observed* Formula GPEtn
(C16:0/18:2) C39H74NO8P 575.49
282.28 C37H67O4+
C18H34O2+
279.25
255.24 C18H31O2‐
C16H31O2‐
142.03
182.06 C2H9NO4P+
C5H13NO4P+ 140.02
180.04 C2H7NO4P‐ C5H11NO4P‐ GPEtn
(C18:2/18:2) C41H74NO8P 599.50
306.29 C39H67O4+
C20H34O2+ 279.25 C18H31O2‐ 142.03
182.06 C2H9NO4P+
C5H13NO4P+ 140.02
180.04 C2H7NO4P‐ C5H11NO4P‐
Diether‐GPEtn C37H78NO6P ?? ?? 239.21 C16H31O‐ ― ― 180.04
140.01 C2H7NO4P‐ C5H11NO4P‐ GPCho C42H82NO8P 504.38
478.37 C26H51NO6P+
C24H49NO6P+ 281.24
255.23 C18H33O2‐
C16H31O2‐
184.10
166.08 C5H15NO4P+
C5H13NO3P+ ― ― Diether‐GPCho C40H85NO6P 464.44
450.41 C24H51NO5P+
C23H49NO5P+ 239.21 C16H31O‐ 184.11 C5H15NO4P+ ― ― GPGro C38H74O10PNa 551.52
511.28 C35H67O4+
C22H42Na2O8P+ 255.24 C16H31O2‐ 198.99 C3H6Na2O5P+ 171.04 211.06 153.02
C3H8O6P‐ C6H12O6P‐
C3H6O5P DG C37H70O5
339.31 313.28 265.26 239.24
C21H39O3+
C19H37O3+
C18H33O+ C16H31O+
281.24
255.23 C18H33O2‐
C16H31O2‐ ― ― 91.04 C3H7O3‐
Diether‐DG C35H72O3
297.32 299.33 253.27
C19H37O2+
C19H39O2+
C17H33O+
241.23
239.19 C16H33O‐
C16H31O‐ ― ― 91.02 C3H7O3‐
Archaeol C43H88O3 373.39
371.27 C23H49O3+
C23H47O3+
371.37 297.32 295.29
C23H47O3‐
C20H41O‐
C20H41O‐ ― ― 91.03 C3H7O3‐
*: Ostrowski et al. (2005) described further, yet unknown negative ions at m/z 137, 153 and 181 as common phospholipid fragments. These findings were partly confirmed in our study. Both fragments at m/z 137.01 and 153.02 where found in the ester bound phospholipids, whereas the ether‐bound phospholipids yielded m/z 137.01. An ion at m/z 181 was not observed in our spectra. Further positive ions described by Ostrowski et al. (2005), at m/z 125, 143, and 165 were exclusively observed in the spectrum of GPGro (Na salt) and may represent Na containing fragments.
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Figure 2‐1. Partial positive (top) and negative (bottom) ToF‐SIMS spectra (m/z 140–800) of phosphatidylethanolamine (GPEtn). This reference standard mainly contains GPEtn carrying two C18:2 or each one C16:0and C18:2 fatty acid chains (C16:0=24%; C18:2=60% of the total fatty acids). See text for a detailed discussion of the spectral patterns. The prominent phosphate peak at m/z 96.97 is out of the displayed range. Peaks labeled with ‘inorg’ are inorganic ions and do not belong to the compound spectrum.
In negative mode, deprotonated molecular ions [M–H]‐ occur at m/z 714.55 (C16:0/C18:2) and 738.55 (C18:2/C18:2). Ions at m/z 697.51 and 671.48 are interpreted as [M–NH3]‐ and [M–C2H7N]‐. Prominent peaks at m/z 96.97 [H2PO4]‐ and 78.96 [PO3]‐ (not shown) and at m/z 140.02 clearly specify the phosphate‐bearing headgroup, as observed in previous studies (Börner et al., 2007; Ostrowski et al., 2005). Fragments at m/z 279.25 (C18:2) and 255.24 (C16:0) can be assigned to fatty acid chains (according to Börner et al., 2007), whereas fragments at m/z 476.29, 452.29 and 434.28 putatively derive from the PE molecule after loss of the fatty acid chains as indicated in Figure 2‐1.
2.4.2. 1,2‐Di‐O‐hexadecyl‐sn‐glycero‐3‐phosphatidylethanolamine (Diether‐
GPEtn)
C37H78NO6P; exact mass 663.56 Da; source: bacteria (Rütters et al., 2001) The positive mass spectrum observed for Diether‐GPEtn reveals a weak protonated molecular ion [M+H]+ at m/z 664.65 (Figure 2‐2). A single, most prominent and
possibly diagnostic peak occurs at m/z 624.67. It is tentatively interpreted as a fragment ion resulting from loss of the ethanolamine group and adduction of Na [M–
C2H7NO+Na]+ (Figure 2‐2). Another, less abundant sodium adduct of a fragment ion may occur at m/z 654.68 [M–CH6N+Na]+.
The negative spectrum shows a clear deprotonated molecular ion [M–H]‐ at m/z 662.49. In addition, minor fragments at m/z 645.47 and 619.46 can be interpreted as [M–NH3]‐ and [M–C2H7N]‐, respectively. It is interesting to see that in both positive and negative mode, peaks expected from lyso‐fragments or aliphatic side chains (around m/z 224) are weak or even missing. Likewise, headgroup fragments of Diether‐GPEtn are only observed in the negative spectrum (Figure 2‐2, Table 2‐1).
Figure 2‐2. Partial positive (top) and negative (bottom) ToF‐SIMS spectra (m/z 100–700) of 1,2‐di‐O‐
hexadecyl‐sn‐glycero‐3‐phosphatidylethanolamine (Diether‐GPEtn). See text for a detailed discussion of the spectral patterns. Peaks labeled with inorganics are contaminant ions and do not belong to the compound spectrum.
Generally, the fragmentation pattern of Diether‐GPEtn is less prominent than that observed for esterbound GPEtn (Figure 2‐1). This is interpreted as reflecting the greater stability of the ether compared to the ester linkage, which appears to hamper rearrangement reactions and cleavage of the side chains under primary ion bombardment.
